Title:  GEN AI Developer

Long Description

Role - GEN AI Developer

Exp - 4-6 years

NP - Imemdiate Only

Key Responsibilities:
1.    Application Development: Build GenAI applications from scratch using frameworks like Autogen (applied or acquired), Crew.ai, LangGraph, LlamaIndex, and LangChain.
2.    Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions.
3.    Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
4.    Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
5.    Fine-tune SLM(Small Language Model) for domain specific data and use cases.
6.    Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends.
7.    Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.
8.    Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases.
9.    LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring.
10.    Responsible AI Practices: Ensure ethical AI practices are embedded in the development process.
11.    innovation.

Required Skills :
1.    Python Programming: Deep expertise in Python for building GenAI applications and automation tools.
2.    Productionization of GenAI application beyond PoCs – Using scale frameworks and tools such as Pylint,Pyrit etc.
3.    LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
4.    Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
5.    Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
6.    Fine-tune SLM(Small Language Model) for domain specific data and use cases.
7.    Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc.
8.    Anti-hallucination and anti-gibberish tools such as Bleu etc.
9.    Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development.
10.    Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. (any two cloud exp.)
11.    Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. (any one is fine)
12.    LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management.
13.    Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations.
14.    RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures.
15.    Data Modernization: Expertise in modernizing and transforming data for GenAI applications.
16.    OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools.
17.    API Integration: Experience with REST, SOAP, and other protocols for API integration.
18.    Data Curation: Expertise in building automated data curation and preprocessing pipelines.
19.    Technical Documentation: Ability to create clear and comprehensive technical documentation.
20.    Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams.
Target Companies – Quantiphi,Datastax,Coforge,HCL,Accenture,Fractal.

Area(s) of responsibility

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